System and method for generating proactive underwriting offers using social media data

ABSTRACT

Pursuant to some embodiments, systems, methods, apparatus and computer program code for proactive underwriting are provided. Pursuant to some embodiments, a computer implemented processing method is provided which includes identifying, by an insurance processing platform, an action by an entity that matches at least a first trigger rule. A proactive underwriting template is selected based on the at least first trigger rule, where the proactive underwriting template defines a number of data items required to complete the proactive underwriting template. The insurance processing platform is operated to automatically collect data associated with the plurality of required data items, and to perform a proactive underwriting analysis using the collected data. An underwriting determination is then rendered based on the proactive underwriting analysis.

CROSS REFERENCE TO RELATED APPLICATIONS

This application is a continuation application of co-pending U.S. patentapplication Ser. No. 13/918,335 entitled “System And Method ForProactive Underwriting Using Social Data” and filed on Jun. 14, 2013,which application is based on, and claims benefit and priority of, U.S.Provisional Patent Application Ser. No. 61/659,749 filed on Jun. 14,2012, the contents of all of which are incorporated herein by referencein their entireties for all purposes.

BACKGROUND

Advances in computing and data processing have led to the creation oflarge sets of data about consumers and businesses. The data includesinformation from a wide variety of sources, including postal data,census and demographic data, and increasingly, data accumulated via userinteraction with social media and other Websites such as Facebook®,Twitter®, Internet forums, question and answer sites (such asStackExchange®), photo sharing sites, and the like.

Frequently, this interaction data can be matched to a specificindividual or business. Advertisers currently use some data to targetadvertising to individuals based on their interests. For example,Google® provides tools for advertisers to place keyword anddemographically-targeted ads on Web pages which are considered to berelevant to consumers based on their search terms and based on theirlocation and other demographic information.

While these targeted advertisements can serve the purpose of providing arelevant and targeted ad or offer to an interested consumer or business,they are unable to provide proactive offers of insurance that arepresented based on some form of underwriting or underwriting analysis.It would be desirable to provide systems and methods that allow theproactive underwriting and presentation of insurance offers toprospective insureds based on the use of social and other data sources.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is block diagram of a system according to some embodiments of thepresent invention.

FIG. 2 is block diagram of a system according to some embodiments of thepresent invention.

FIG. 3 is block diagram of a system according to some embodiments of thepresent invention.

FIG. 4 is a flow diagram of a process according to some embodiments ofthe present invention.

FIG. 5 is a diagram of some exemplary screen according to someembodiments of the present invention.

FIG. 6 is a diagram of a portion of an exemplary data storage tableaccording to some embodiments of the present invention.

DETAILED DESCRIPTION

Pursuant to some embodiments, systems, methods, apparatus and computerprogram code for proactive underwriting are provided. Pursuant to someembodiments, a computer implemented processing method is provided whichincludes identifying, by an insurance processing platform, an action byan entity that matches at least a first trigger rule. A proactiveunderwriting template is selected based on the at least first triggerrule, where the proactive underwriting template defines a number of dataitems required to complete the proactive underwriting template. Theinsurance processing platform is operated to automatically collect dataassociated with the plurality of required data items, and to perform aproactive underwriting analysis using the collected data. Anunderwriting determination is then rendered based on the proactiveunderwriting analysis.

As used herein, the term “proactive underwriting” refers to theunderwriting, evaluation, rating, offering, quoting and/or pricing ofinsurance for one or more entities without necessarily requiring aspecific request or application from those entities. For example, aswill be described further herein, a restaurant may be proactivelypresented with an offer of a small commercial insurance policy based oninformation obtained about the restaurant using techniques describedherein. The offer may be unsolicited or requested by the restaurant. Insome embodiments, the term “proactive underwriting” further encompassesthe underwriting, evaluation, rating, offering, pricing or otherwiseevaluating insurance for one or more entities in response to a requestor inquiry such that some aspect or portion of the insurance isproactively provided using features of the present invention. Forexample, a restaurant may submit a request for a small commercial linesinsurance policy, and the proactive underwriting system of the presentinvention may proactively obtain one or more items of informationassociated with the application (e.g., such that the restaurant need notprovide all of the information needed to underwrite or price thepolicy—some of the information may be gathered or obtained using thesystem of the present invention).

Pursuant to some embodiments, a “template” specifying the types of dataneeded for the proactive underwriting of a policy or offer of insurancemay be defined by the insurance company. For example, each form ofinsurance implemented using the present invention may have one or more“templates” that define the specific items of data needed for theproactive underwriting process to occur (such as, for example, the nameof the insured, an address of the insured, etc.).

Prior to discussing features of some embodiments, an illustrative (butnot limiting) example will be provided. This example will be referencedthroughout the remainder of this specification to assist in describingfeatures of some embodiments. In the illustrative example, an operatoror administrator of a system pursuant to the present invention hasidentified (either automatically by operation of the system orotherwise) that certain types of information necessary for underwritingsmall commercial lines insurance policies may be reliably collected fromcertain types of data sources (including, for example, social media datasources). More particularly, the operator or administrator may haveidentified that certain types of restaurants may be accuratelyunderwritten for small commercial lines insurance policies byproactively collecting information associated with the restaurant. Thenature and type of the collected information may be identified based ona historical analysis of claims and loss data associated with similarrestaurants.

In the illustrative example, the operator or administrator hasdetermined that proactive underwriting may be performed for restaurantsfor small commercial lines insurance policies. For example, a restaurantmay be proactively offered a policy based on data collected andaggregated using the system of the present invention, and/or arestaurant that requests a policy may not need provide the fullapplication data typically required of a restaurant (as some portion ofthe data needed for underwriting may be collected and aggregated usingthe system of the present invention).

As a further specific illustrative (but not limiting) example, thesystem of the present invention may collect information required by acommercial lines policy “template” associated with a restaurant. Forexample, a commercial lines policy template for a restaurant may specifythe data to be collected for proactive underwriting, including, forexample, the type of restaurant (e.g., whether the restaurant providestable service, whether the restaurant provides table service withalcohol, etc.), the size of the restaurant (e.g., the estimated grossreceipts, the square footage of the restaurant, the number of tables, orthe like), features of the restaurant (e.g., whether the restaurant hasone or more dance floors, whether the restaurant has video gamemachines, whether the restaurant is a sports bar, etc.). Thisinformation may be collected and aggregated from a number of differentsources, including social media sources including Yelp.com, Google.com,Facebook.com, Instagr.am, or the like. These illustrative examples willbe referenced throughout this disclosure to describe features of someembodiments of the present invention. Those skilled in the art, uponreading this disclosure, will appreciate that these examples are forillustration only, and that features of some embodiments may be usedwith desirable results when used in conjunction with other types ofinsurance (e.g., such as personal lines insurance, large commercial, orthe like).

Further details, features and advantages of proactive underwritingpursuant to the present invention will become apparent upon reading thefollowing detailed description. Features of some embodiments will now bedescribed by first referring to FIG. 1, which is a block diagram of aprocessing platform 100 according to some embodiments of the presentinvention. The platform 100 may, for example, facilitate proactiveunderwriting using demographic, search, community, social and businessnetwork based data such as information published by individuals orbusinesses (e.g., via Twitter, Facebook, Google+, or the like), as wellas information shared by individuals or businesses via searches,comments, postings, or the like.

For illustrative, but not limiting, purposes such information may bepublished by sites or networks including ebay.com, Facebook.com,LinkedIn.com, Twitter.com, Blogger.com, MySpace.com, Friendster.com,Google+, Youtube, Pinterest and other similar sites. Information mayalso be obtained from applications (such as those provided through theApple® store, the Android® marketplace or the like) and devices (such asmobile phones, navigation systems, desktop computers or the like).Information may also be obtained from monitoring or telematics devices.For example, fitness and health data may be obtained fromwireless-enabled scales (which measure and provide data regarding weightand body mass), wearable devices that measure data such as the number ofcalories expended, the number of steps walked, the quality of sleep, orthe like. Such data may be obtained from one or more data repositoriessuch as those provided by Fitbit® or the like. Any or all of thesesources of data will be generally referred to herein as “social data”sources. For clarity and ease of exposition, individuals and businessesusing features of the present invention to receive insurance servicesand information may generally be referred to herein as “consumers” or“individuals”. In some embodiments, the term “entity” may be used torefer to an individual, consumer, business, business representative, orother entity that performs an action (such as makes a comment, updates awebsite, asks a question, updates a status, or the like) that triggers aproactive underwriting process. In some embodiments, the “entity” may bea prospective insured. In some embodiments, the “entity” may be an agentof a prospective insured, or a party somehow associated with aprospective insured.

As used herein, the term “rendering an underwriting decision” may beused to refer to providing a price quotation associated with aninsurance product that has been evaluated using the present invention,providing a bindable quotation, providing an indicative quotation (whichmay require further information before it becomes bindable), or thelike. As used herein, the term “communicating” an offer of insurance maybe used to refer to providing a link to an insurance offer or quotation(such as a bindable or indicative quotation), providing a full offer(e.g., as an attachment to an email or as an email or item of postalmail), or otherwise communicating information to a prospective insured.

According to some embodiments, a proactive underwriting processingplatform 110 may be provided for underwriting, evaluating, rating,offering, pricing or quoting insurance based on data from a variety ofsources, including social network sites, operator entry, Websites, orthe like. By way of example only, the proactive underwriting processingplatform 110 may use data from such sources to both identify actions ortrigger events that are associated with a request or need for insurance,as well as to collect data (pursuant to the relevant “template”) neededto perform proactive underwriting for a particular offer of insurance.

In embodiments described herein, the proactive underwriting processingplatform 110 may be operated to both identify activities, searches orother events that may trigger a proactive underwriting analysis, and topresent an offer or information associated with a proactive underwritinganalysis (e.g., such as a quote or offer of insurance) to a consumer orother entity. Pursuant to some embodiments, the proactive underwritingmay be performed for a number of different types of insurance policies,including personal lines, workers compensation, health, group benefits,and other commercial policies. Pursuant to some embodiments, theprocessing platform 110 receives data from a wide variety of sourcesincluding one or more social media or other websites or properties120-130 and devices 102, 104, 106. The data received are used toidentify potential triggers or other events that cause a proactiveunderwriting process to be performed in association with a consumer orentity, and to retrieve the data needed for the underwriting process(pursuant to the relevant template). Further, the processing platform110 may transmit data and notifications to consumers and insuredindividuals and businesses directly to devices 102, 104 or 106 orthrough one or more social media sites 120-130.

As used herein, devices including those associated with the proactiveunderwriting processing platform 110, and any other device describedherein may exchange information via any communication network 160 whichmay be one or more of a Local Area Network (LAN), a Metropolitan AreaNetwork (MAN), a Wide Area Network (WAN), a proprietary network, aPublic Switched Telephone Network (PSTN), a Wireless ApplicationProtocol (WAP) network, a Bluetooth network, a wireless LAN network,and/or an Internet Protocol (IP) network such as the Internet, anintranet, or an extranet. Note that any devices described herein maycommunicate via one or more such communication networks.

Sites 120-130 may store, publish or otherwise provide access toinformation about consumers or other entities that may have insuranceneeds. For example, a consumer with a Facebook account may post statusupdates, information and comments to Facebook, and Facebook may publishor otherwise make the status updates, information or comments availableto authorized individuals or entities. A new business may create aFacebook or Twitter account, or may launch a new Website for theirbusiness. Data associated with these events or activities may be used toidentify triggers to initiate proactive underwriting processes pursuantto the present invention.

In some embodiments, one or more of the sites 120-130 may publish orotherwise disseminate the information via an application programminginterface (“API”), an RSS feed, or some other structured format. Theinformation may be analyzed or used by the proactive underwritingprocessing platform 110 on an individual item basis or on an aggregatebasis with other information in order to: (i) identify trigger eventsindicating a request or need for insurance, and (ii) collect data(pursuant to a relevant template) needed to perform the proactiveunderwriting. Further the data may be combined with one or more otherdata sources, such as publicly available data provided by the U.S.Census Bureau or the like. Pursuant to some embodiments, data collectedabout individuals may be indexed to aggregate data across a wide varietyof data sources as well as to de-identify any personally identifiableinformation. For example, a record about a consumer may include anon-personally identifiable identifier used to associate informationabout the consumer such as name, address, one or more social media usernames, telephone numbers, email addresses, with information associatedwith characteristic data collected pursuant to the operation of thepresent invention.

As shown, the proactive underwriting processing platform 110 may includea number of modules or components, including one or more underwritingmodules 112, quoting modules 114, issuing modules 116, notificationmodules 118 and group administration module 119. Proactive underwritingprocessing platform 110 may be deployed as a number of differentplatforms in communication with each other (for example, one processingplatform may be deployed as a platform to identify trigger events bymonitoring multiple data sources, while another may be deployed toperform underwriting analysis and data collection). Pursuant to thepresent invention, the notification modules 118 may be used to transmitinformation to eligible individuals, to service providers, and to otherentities, including information relating to offers of insuranceresulting from operation of the proactive underwriting platform of thepresent invention.

As will be described further below, the underwriting modules 112 may beused in conjunction with the creation and updating of one or more ratingschedules for use in pricing and rating insurance policies or increating insurance benefits or other offers in response to one or moretrigger events pursuant to embodiments of the present invention. Forexample, in some embodiments, the underwriting modules 112 are used toanalyze both conventional underwriting data such as historical lossinformation in conjunction with social and business network based datafor use in rating and pricing business insurance policies.

Referring still to FIG. 1, the quoting and issuing modules 114 and 116may be used in conjunction with the quoting, rating and pricing ofinsurance policies during proactive underwriting processing pursuant tothe present invention. Note that the underwriting module 112, quotingmodule 114, and/or issuing module 116 may be associated with varioustypes of insurance policies, including automobile and home insurancepolicies, for individuals and/or companies.

Although a single proactive underwriting processing platform 110 isshown in FIG. 1, any number of such devices may be included. Moreover,various devices described herein might be combined according toembodiments of the present invention. For example, in some embodiments,the proactive underwriting processing platform 110 and modules 112-119might be co-located and/or may comprise a single apparatus.

The proactive underwriting processing platform 110 and the modules112-119 may also access information in one or more databases 170, 180and 190. The databases may include, for example, risk characteristicdata 170, historical loss data 180 associated with previously-issuedinsurance policies, and policy data 190 associated with active policies.As will be described further below, the policy data 190 may be used toprocess information associated with trigger events related to requeststo update an insurance policy or trigger events that may suggest that apolicy change may be necessary or appropriate. That is, while in someembodiments (such as the illustrative example introduced above), theproactive underwriting process is performed for new customers of theinsurance company, in some embodiments, the proactive underwritingprocess may be performed for existing customers of the insurance company(e.g., based on the identification of trigger data that indicates thatan update or a change to an existing policy is needed or appropriate).

Referring now to FIG. 2, one embodiment of the present invention isshown for proactive underwriting to provide targeted offers of insurancebased on one or more identified triggers in social data or other datasources. As shown in FIG. 2, the proactive underwriting processingplatform 200 communicates via network 210 to send data to, and receivedata from, a plurality of user devices 220 (such as mobile phones,computers, or the like), a plurality of data sources 230 (such as socialnetworking sites, public data sources, or the like), and one or moreunderwriting target(s) 240 to enable an insurance company to provide theunderwriting target(s) 240 with offers of insurance that are proactivelyunderwritten based on the identification of one or more trigger events.

Platform 200 also may include a number of devices or components,including computer processor(s) 275 and text processing units 250. Thecomputer processor 275 and the text processing unit 250 may include oneor more conventional microprocessors and may operate to executeprogrammed instructions to provide functionality as described herein.Among other functions, the computer processor 275 and/or the textprocessor 250 may access and retrieve information from data source(s)230 via network interface unit 260 and input/output controller 270 viasystem bus 280.

Data identifying one or more sets of trigger rules and conditions may bestored in a data store 296. The trigger data may be specified by aninsurance company or other entity to identify comments, actions, orother events that may trigger a particular type of proactiveunderwriting process. The trigger data may be applied to data monitoredor analyzed from a variety of data sources, and may be formed as rulesor conditions that must be met in order for a proactive underwritingprocess to be performed.

For example, in a mode of operation in which the proactive underwritingprocessor is operated to identify individuals or entities who may have aneed for an insurance policy (either new or updated), the text processor250 may be operated to scan existing forums, social media sites, andother web sites to analyze data to identify data that matches one ormore trigger rules. The data analyzed may include forum posts, comments,Web blog posts, images, preferences (such as preferences or profile dataassociated with user accounts on sites such as Google+, Facebook, or thelike), Twitter posts, etc. The text processor 250 may be programmed toidentify different types of statements or comments that are relevant toone or more trigger rules.

The proactive underwriting processing platform 200 may further include aprogram memory 282 that is coupled to the computer processor 275. Theprogram memory 282 may include a random access memory 284 and a readonly memory 286. System memory 282 is further coupled via bus 280 to oneor more fixed storage devices 290, such as one or more hard disk drives,flash memories, tape drives or other similar storage devices. Storagedevices 290 may store one or more application programs 292, an operatingsystem 294, and one or more databases such as a trigger database 296 forstoring data identifying rules and conditions for a plurality ofdifferent triggers as well as a template database 298 for storing datadefining the data to be collected to underwrite certain forms ofinsurance. Each trigger may be associated with one or more templates andeach template may be associated with one or more triggers.

Platform 200 may be, according to some embodiments, accessible via aGraphical User Interface (GUI) rendered at least in part by input/outputcontroller 270. The GUI might be used, for example, to dynamicallydisplay information associated with templates, triggers, triggers thathave occurred, or the like.

Referring still to FIG. 2, the platform 200 performs processing toreceive, process and extract relevant information from data source(s)230 (such as social network data, search queries, blog comments, forumposts, etc.). The processing and extraction of information from the datasource(s) 230 may take one or more of a number of different forms. Forexample, the processing platform 200 may monitor or search for activityassociated with a number of triggers stored in trigger database 296. Thetriggers may be applied to data retrieved from data source(s) 230, or,in some embodiments, may be used to control queries of data source(s)230 to identify data that match one or more triggers. For example, inthe illustrative example introduced above, a set of trigger rules may bedefined to attempt to identify restaurants that may need smallcommercial insurance policies. Those rules may include queries or searchterms or conditions that must be met in order for the trigger to besatisfied. The search and processing of processing platform 200 mayinvolve the use of natural language processing techniques to determinewhether certain search, posting, or other activities of consumers orother entities contain, in substance, information that satisfies one ormore triggers such that further proactive underwriting processing may beperformed. Pursuant to some embodiments, the processing may includingmonitoring a plurality of social media data sources that may includemonitoring a number of different social media websites (such as, forexample, Twitter, Facebook, other websites, or the like). In someembodiments, the monitoring may include monitoring different webpageswithin a website (for example, different Facebook pages may be monitoredfor changes, such as a restaurant owner's personal Facebook page and therestaurant's official Facebook page).

Once a trigger has been satisfied, the processing platform 200 operatesto collect data to satisfy the requirements of a template associatedwith the trigger, as described further below in conjunction with FIG. 4.

It is contemplated that the processing platform 200 may process data andinformation in one or more languages, such English, French, Arabic,Spanish, Chinese, German, Japanese and the like. In an exemplaryembodiment, underwriting analysis by the platform 200 also can beemployed for sophisticated text analyses, wherein text can be recognizedirrespective of the text language. The relationships between the variouswords/phrases can be clarified by using an insurance rules engines forclassifying words/phrases as a predictor of certain underwriting risk oras a predictor of intent or interest (e.g., to determine whether anindividual or entity is interested in or needs an insurance proposal).

Reference is now made to FIG. 3, in which an embodiment of a system 300configured to identify a trigger event or set of events which cause aproactive underwriting process to be performed. As shown, system 300includes a mobile device 310 operated by an individual such as an owner,operator, or other employee of an entity. The individual is shownoperating the mobile device 310 to post a “tweet” on twitter.com usinghis Twitter account (“@Joes_restaurant”). The tweet is a messageannouncing that the individual is inviting followers or other readers to“Join us on opening night at Joe's Restaurant!”, and sending the messagewill cause a website 320 (e.g., twitter.com) to post an updateassociated with Joes_restaurant's Twitter feed with the content of themessage. Pursuant to some embodiments, Twitter feeds (as well as datafrom a variety of other sites and forums) are monitored by the proactiveunderwriting platform 340 to identify messages and posts which meet oneor more trigger data 350 or rules associated with one or moreunderwriting templates 355.

The data from the site 320 may be received and parsed using one or moreapplication programming interfaces (“APIs”) 370 which allow data from alarge number of different sites to be collected and monitored by theproactive underwriting platform 340. If the proactive underwritingplatform 340 determines (upon processing the relevant underwritingtemplates) that an offer of insurance may be made, the offer may becommunicated to the individual or entity via a communications platform380. For example, the communications platform 380 may cause the offer tobe communicated to the individual or entity via postal mail, via email,phone or as a message in a social network (such as a direct message viaTwitter, or the like). In some embodiments, the communications platform380 may cause the offer to be communicated to the individual or entityas a display ad or other message or offer online.

In the illustrative example introduced above, an insurance company orother entity operating the proactive underwriting platform 340 hasdetermined that certain types of insurance may be underwritten orotherwise processed using the system of the present invention. Moreparticularly, in the illustrative example, an insurance company hasdetermined that certain forms of commercial lines insurance policies(such as those for certain classes of restaurants) may be underwrittenusing the proactive underwriting platform 340. The type and nature ofthe underwriting performed may vary for different types of businesses,and as a result, the insurance company has established one or moreunderwriting templates 355 which define the type and nature ofinformation that is required to perform a proactive underwriting processfor a particular entity. For example, an underwriting template 355 for asmall restaurant may require different underwriting data than anunderwriting template 355 for a large restaurant. Pursuant to someembodiments, information associated with each of a number of templatesmay be stored at or accessible to the proactive underwriting platform340 for use in determining the type of information that must be obtainedbefore a quote or policy may be fully underwritten.

Further, the trigger or characteristic data 350 stored at or accessibleto proactive underwriting platform 340 may include a number of rules orconditions that must be satisfied before a proactive underwritingprocess is initiated. As an illustrative example, one trigger orcharacteristic rule may be designed to identify posts, comments,announcements, messages or other social data that may indicate that arestaurant in a given geographical region is opening. The rules mayinclude natural language processing rules that are selected to reliablyidentify data that tends to indicate the opening of a restaurant. Insome embodiments, the rules may also include geographical limitations(e.g., to identify potential restaurant openings in a geographicalregion in which insurance coverage by the insurance company is offered).

Individuals may create content and interact with third party websitesusing any of a number of different types of computing devices, includingdesktop computers, tablet computers, or mobile devices such as themobile device 310. The mobile device 310 may be any of a number ofdifferent types of mobile devices that allow for wireless communicationand that may be carried with or by a user. For example, in someembodiments, mobile device 310 is an iPhone® from Apple, Inc., aBlackBerry® from RIM, a mobile phone using the Google Android® operatingsystem, a portable or tablet computer (such as the iPad® from Apple,Inc.), a mobile device operating the Android® operating system or otherportable computing device having an ability to communicate wirelesslywith a remote entity such as a third party website 320.

FIG. 4 illustrates a method that might be performed, for example, bysome or all of the components and elements of the proactive underwritingsystem described with respect to FIG. 1 or 2. The flow charts describedherein do not imply a fixed order to the steps, and embodiments of thepresent invention may be practiced in any order that is practicable.Note that any of the methods described herein may be performed byhardware, software, or any combination of these approaches. For example,a computer-readable storage medium may store thereon instructions thatwhen executed by a machine result in performance according to any of theembodiments described herein.

The process 400 may be performed in response to an action associatedwith an individual or entity to cause a proactive underwriting analysisto be performed such that, in some embodiments, an offer of insurancemay be provided to the individual or entity. Pursuant to someembodiments, process 400 includes a step 402 of monitoring selected datasources. Processing at 402 may include operating one or more “listeners”or data collection routines to receive, process and extract relevantinformation from one or more data source(s) (such as the data source(s)230 of FIG. 2). The data sources may include social network sites (suchas Twitter.com, Facebook, Instagr.am, Flickr, blog comments, websitechanges, forum posts, or the like). The monitoring and extraction ofinformation from the data source(s) 230 may take one or more of a numberof different forms. For example, the proactive underwriting platform maymonitor or search for activity associated with certain definedcharacteristics associated with one or more types of insurance, or oneor more underwriting templates associated with particular types ofinsurance. The data sources monitored at 402 may be monitored in realtime or in a batch basis.

Processing continues at 404 where the proactive underwriting system isoperated to analyze the data from the monitored data sources to identifydata that meets one or more trigger conditions. The trigger conditionsmay be established as rules that must be satisfied for a particularproactive underwriting analysis to be performed. For example, continuingthe illustrative example introduced above, a trigger condition may bethe identification of data that tends to signify that a new restaurantis being opened (or reopened) in a particular geographical area. Therules associated with the trigger condition may specify a particulargeographical region, a particular type of restaurant, and possibleadditional factors (e.g., such as weighting factors that may be used toassess the likely credibility of a data source). Different data sourcesmay have different trigger conditions or rules. For example, arestaurant review site (such as Yelp.com) that includes editorial datafrom professional editorial staff may be monitored to identify newrestaurants (or restaurants that have been recently added to the datasource). That is, in some embodiments, the trigger condition or rule maybe the activity of a data source adding information about a particularindividual or entity (such as the addition of a new restaurant to areview site).

Other trigger events or conditions may be the identification of certainkeywords or other trigger language that have been correlated to indicatethat a particular type of insurance may be desired or appropriate.Continuing the illustrative example, a trigger event or conditionassociated with identifying new restaurant openings or expansions (andrelated to the underwriting of a small commercial lines policy) may besatisfied by a Twitter message from a Twitter account of a restaurantthat announces an “opening” or “expansion”. As another example, atrigger event to identify a restaurant expansion may be satisfied by theposting of one or more pictures on an Instagr.am account with a tag orcaption noting “Joe's Restaurant is expanding! Check out our new dancefloor!”

Pursuant to some embodiments, multiple data sources may be requiredbefore a trigger condition or rule is satisfied to avoid errors orwasted efforts in performing the proactive underwriting of the presentinvention. Processing at 404 continues until a trigger rule has beenmet. In some embodiments, processing at 404 may be performed in multiplesimultaneous processes to analyze multiple data sources for multipletrigger conditions.

Once a trigger condition has been identified as having been satisfied,processing continues at 406, where an applicable underwriting templateis identified. For example, each trigger rule or condition may beassociated with a relevant underwriting template. Continuing theillustrative example, the satisfaction of trigger conditions associatedwith a “restaurant” having an “opening” or “expansion” in Arizona mayresult in the identification of a specific small commercial linesunderwriting template that was created for new or expanding restaurantsin Arizona.

Each template may specify one or more items of data that are required toperform a proactive underwriting process for the associated type ofinsurance. For example, a template for a small commercial linesunderwriting template for restaurants in Arizona may require that thefollowing data be identified for use in proactive underwriting: therestaurant name, a type of the restaurant, a location of the restaurant(e.g., Address, City, State, and Zip), and a size of the restaurant. Insome embodiments, the template may specify one or more additional itemsof data that should be collected if possible (but may not necessarily berequired for the proactive underwriting process). For example, thefollowing are non-limiting examples of optional data that may becollected: an estimate of the total receipts of the restaurant (whichmay be determined using comparable restaurant data for the geographicarea), an estimate of the total liquor receipts of the restaurant, theestimated class or SIC code of the restaurant, the number of floors ofthe restaurant, the size of the restaurant, the number of gamingmachines in the restaurant, or the like. Those skilled in the art, uponreading this disclosure, will appreciate that any number of data itemsmay be required by a template, and any number of data items may beidentified as optional for a template (the selection and requirements ofthe data for each template may be dictated by a modeling process or byother analysis of underwriting and claims data).

Process 400 continues with accessing data sources at 408. Data sources,such as the data sources 230 of FIG. 2, may be accessed, queried orotherwise analyzed to attempt to identify the data required by theunderwriting template identified at 406. For example, in theillustrative example introduced above, a proactive underwriting processfor a new restaurant is being performed, and the relevant underwritingtemplate requires that data be collected which identifies therestaurant. Processing at 406 may include generating search queries toretrieve information from one or more data sources that can be used toidentify the restaurant. Similar processing may be performed for eachitem of data required by the underwriting template being processed.

Process 400 continues at 410 with the performing of text mining of thosedata sources to obtain the information required by the underwritingtemplate identified at 406. Process 400 continues at 412 with thecombining of traditional underwriting data with the data obtained bytext mining, and outputting an underwriting decision at 414.

In other embodiments, the social data may be used in conjunction withone or more predictive models to take into account a large number ofunderwriting parameters. The predictive model(s), in variousimplementation, may include one or more of neural networks, Bayesiannetworks (such as Hidden Markov models), expert systems, decision trees,collections of decision trees, support vector machines, or other systemsknown in the art for addressing problems with large numbers ofvariables. Preferably, the predictive model(s) are trained on prior dataand outcomes known to the insurance company. The specific data andoutcomes analyzed vary depending on the desired functionality of theparticular predictive model. The particular data parameters selected foranalysis in the training process are determined by using regressionanalysis and/or other statistical techniques known in the art foridentifying relevant variables in multivariable systems. The parameterscan be selected from any of the structured data parameters stored in thepresent system, whether the parameters were input into the systemoriginally in a structured format or whether they were extracted frompreviously unstructured text, such as from text based social data.

In some embodiments, the data collected and mined at 410 (and othersteps herein) may be verified or validated. For example, if a name andaddress of a business is collected, other data sources may be consultedto verify or validate the actual business name. For example, businessregistration data sources may be consulted (such as data sourcesproviding Secretary of State or other corporate records). In someembodiments, the verification or validation may be provided by adifferent entity than the prospective insured, while in someembodiments, the verification or validation may be performed byobtaining further data or information from the prospective insured. Insome embodiments, additional information (such as traditionalunderwriting information) may be appended to or used in conjunction withthe data collected from the data sources at 402.

Pursuant to some embodiments, if the underwriting decision at 414 isthat an insurance offer may not be made given the data collected for theunderwriting template, a further process may occur in which atraditional underwriting process is performed. For example, if theunderwriting decision at 414 is not to underwrite, an underwritingreferral may be done by the system automatically e-mailing ortransmitting the tagged electronic application file to an underwriterfor further review. If more than one underwriter is available to receivethe referral of the file, then the computer system may automaticallyselect the underwriter who is to receive the referral based on one ormore factors such as one or more attributes of the insurance/applicant,the underwriter's qualifications and/or experience, the underwriter'scurrent workload, etc. The underwriter's role, at this point, is toreview the file, confirm that the referral is warranted, proceed withfurther analysis/investigation of the prospective insured, and then makean underwriting decision based on the additional underwriting performedwhich was triggered by the data collected pursuant to the underwritingtemplate.

In embodiments where a trigger event or action has been identified, datahas been collected for an appropriate underwriting template, and anunderwriting decision has been received, processing may includenotifying the prospective insured of the insurance proposal. Suchnotification may be performed by an email, direct message, mail, ortelephone call in which details of the insurance offer are communicatedto the prospective insured.

FIG. 5 illustrates some exemplary screens that may be used, displayed orprovided, for example, by some or all of the components and elements ofthe proactive underwriting system described with respect to FIG. 1 or 2.The proactive underwriting processing platform 110, as shown in FIG. 1,may use data from such sources such as an online forum 510 and/or anauction site 520 to identify actions or trigger events that areassociated with a request or need for insurance. Pursuant to someembodiments, the proactive underwriting may be performed for a number ofdifferent types of insurance policies, such as in this exemplary case, apersonal lines automobile policy. The proactive underwriting processingplatform 110, as shown in FIG. 1, may use data from either or both theonline forum 510 associated with a particular type of autombile and theauction site 520 for the same automobile as a trigger event(s) that isassociated with a need for insurance for that particular automobile andthen notify a potential consumer or user with an insurance offer forsuch an automobile. Such notification may be performed usingnotification module 118, as shown in FIG. 1, by an electronic mailmessage 530 that contains an embedded insurance offer 540. A user mayclick or access the insurance offer 540 to bind the offer or accessadditional information related to the offer.

Referring to FIG. 6, a proactive underwriting table 600 is shown thatmay be stored in one or more databases 170, 180 and 190 as shown in FIG.1 The table 600 may include, for example, entries identifying certaintrigger rules and events and certain underwriting information associatedwith the rules and events. The table may also define fields 610, 620,630, 640 and 650 for each of the entries. The fields 610, 620, 630, 640and 650 may, according to some embodiments, specify: a trigger ruleidentification 610, a trigger event 620, a first underwriting data 630,a second underwriting data 640 and a third underwriting data 650 Theinformation in the table 600 may be created and updated, for example,whenever data is analyzed and/or new interactions occur. Other triggerevents or conditions may be the identification of certain keywords orother trigger language that have been correlated to indicate that aparticular type of insurance may be desired or appropriate. Theproactive underwriting system may specify one or more items of data thatare required to perform a proactive underwriting process for theassociated type of insurance. For example, the proactive underwritingsystem may require that, upon detection of a trigger rule TR-123 relatedto a “new car,” the following data be identified for use in proactiveunderwriting: Car Type, Car Model and Location.

The following illustrates various additional embodiments of theinvention. These do not constitute a definition of all possibleembodiments, and those skilled in the art will understand that thepresent invention is applicable to many other embodiments. Further,although the following embodiments are briefly described for clarity,those skilled in the art will understand how to make any changes, ifnecessary, to the above-described apparatus and methods to accommodatethese and other embodiments and applications.

Although specific hardware and data configurations have been describedherein, not that any number of other configurations may be provided inaccordance with embodiments of the present invention (e.g., some of theinformation associated with the databases described herein may becombined or stored in external systems). For example, some or all of thesteps of the process of FIG. 4 may be performed using one or moreprocessors or computer systems. For example, in some embodiments, asingle processor or computer system may be programmed to perform all ofthe steps of a process. In some embodiments, different steps of aprocess may be performed using one or more different processors. As aresult, as used herein, the term “by a processor” may refer to a single(or the same) processor or computer system, or it may refer to multiple(or different) processors or computer systems.

The term “computer-readable medium” as used herein refers to anynon-transitory medium that provides or participates in providinginstructions to the processor of the computing device (or any otherprocessor of a device described herein) for execution. Such a medium maytake many forms, including but not limited to, non-volatile media andvolatile media. Non-volatile media include, for example, optical,magnetic, or opto-magnetic disks, or integrated circuit memory, such asflash memory. Volatile media include Dynamic Random Access Memory(“DRAM”), which typically constitutes the main memory. Common forms ofcomputer-readable media include, for example, a floppy disk, a flexibledisk, hard disk, magnetic tape, any other magnetic medium, a CD-ROM,DVD, any other optical medium, punch cards, paper tape, any otherphysical medium with patterns of holes, a RAM, a PROM, an EPROM orElectronically Erasable Programmable Read-Only Memory (“EEPROM”), aFLASH-EEPROM, any other memory chip or cartridge, or any othernon-transitory medium from which a computer can read.

Various forms of computer readable media may be involved in carrying oneor more sequences of one or more instructions to a processor (such asthe processor 275 of FIG. 2, or any other processor of a devicedescribed herein) for execution. For example, the instructions mayinitially be borne on a magnetic disk of a remote computer (not shown).The remote computer can load the instructions into its dynamic memoryand send the instructions over an Ethernet connection, cable line, oreven telephone line using a modem. A communications device local to acomputing device (e.g., a server) can receive the data on the respectivecommunications line and place the data on a system bus for theprocessor. The system bus carries the data to main memory, from whichthe processor retrieves and executes the instructions. The instructionsreceived by main memory may optionally be stored in memory either beforeor after execution by the processor. In addition, instructions may bereceived via a communication port as electrical, electromagnetic oroptical signals, which are exemplary forms of wireless communications ordata streams that carry various types of information.

The present invention has been described in terms of several embodimentssolely for the purpose of illustration. Persons skilled in the art willrecognize from this description that the invention is not limited to theembodiments described, but may be practiced with modifications andalterations limited only by the spirit and scope of the appended claims.

What is claimed:
 1. A computer implemented method, comprising:continually monitoring, by a proactive coverage generation platformprocessor implementing a plurality of application program interfaces viaa network interface unit connected to the Internet, a plurality of datasources including: RSS data feeds, and two or more of social datasources, entity web sites, forum web sites, blog web sites, auction websites, or data repositories, to receive, process, and extract datarelating to entities with potential coverage needs, wherein additionaldata received includes one of: biometric data from wearable devices ornavigation data from telematics devices, analyzing the extracted dataand identifying, by the proactive coverage generation platformprocessor, whether the extracted data includes data indicative of anaction by an entity that matches at least a first trigger ruleindicative of a coverage need, the first trigger rule comprising one ofa plurality of trigger rules stored in a trigger database; responsive toidentification of the action that matches the at least first triggerrule, selecting, by the proactive coverage generation platformprocessor, one of a plurality of proactive coverage generationunderwriting templates stored in a template database, said proactivecoverage generation underwriting template being associated with said atleast first trigger rule and defining a plurality of required data itemsfor generating an underwriting determination; obtaining, via the networkinterface unit, from a plurality of data sources, the one or more of theplurality of required data items required in accordance with thetemplate associated with the at least first trigger rule; performing, bythe proactive coverage generation platform processor, a proactiveunderwriting analysis, said proactive underwriting analysis beingperformed without a request from said entity to perform saidunderwriting analysis; generating, by an underwriting module, a quotingmodule, and a rating schedule database coupled to the proactive coveragegeneration platform processor, an underwriting determination comprisinga price quotation for a risk mitigation product for the entity based onsaid proactive underwriting analysis; generating, by a notificationmodule coupled to the proactive coverage generation platform processor,an electronic communication including the price quotation for the riskmitigation product; and transmitting, by the network interface unit to auser device corresponding to the entity, the electronic communicationincluding the price quotation for the risk mitigation product.
 2. Thecomputer implemented method of claim 1, wherein analyzing the extracteddata and identifying whether the extracted data includes data indicativeof the action by the entity that matches at least the first trigger ruleindicative of the coverage need comprises applying natural languageprocessing to the extracted data by the proactive coverage generationplatform processor.
 3. The computer implemented method of claim 1,wherein the data indicative of an action by an entity that matches atleast a first trigger rule indicative of a coverage need is unrelated toany risk mitigation policy, application for risk mitigation coverage, oractivity by an agent that sells risk mitigation coverage.
 4. Thecomputer implemented method of claim 1, wherein the obtaining, from theplurality of data sources, the one or more of the plurality of requireddata items, is responsive to accessing, by the proactive coveragegeneration platform processor via the network interface unitimplementing the plurality of application program interfaces and withoutinteraction with the entity, the plurality of data sources to obtain oneor more of the plurality of required data items required in accordancewith the template associated with the at least first trigger rule. 5.The computer implemented method of claim 1, wherein performing theproactive underwriting analysis comprises applying, by the proactivecoverage generation platform processor, a computerized predictive modeltrained to analyze relevant underwriting parameter variables in amultivariable system to said collected data.
 6. The computer implementedmethod of claim 1, wherein the electronic communication comprises one ofan electronic mail message and a social media network direct message. 7.The computer implemented method of claim 1, wherein the data indicativeof the action by the entity that matches at least the first trigger ruleis at least one of: (i) data indicative of an Internet comment, (ii)data indicative of an Internet status, (iii) data indicative of anInternet action by said entity, (iv) data indicative of a Webpagechange, and (v) data indicative of an Internet communication.
 8. Thecomputer implemented method of claim 7, wherein the data indicative ofthe action is associated with geographical data associated with alocation of said entity, wherein said selecting a proactive coveragegeneration underwriting template further includes: selecting, by theproactive coverage generation platform processor, the proactive coveragegeneration underwriting template available for use in said location ofsaid entity.
 9. A computer system comprising: a data storage devicestoring a plurality of proactive underwriting templates and a pluralityof trigger rules, each of said plurality of proactive underwritingtemplates being associated with at least a first trigger rule of theplurality of trigger rules and defining a plurality of required dataitems for generating an underwriting determination; and a proactiveunderwriting platform processor configured to: continually monitor, by anetwork interface unit by a plurality of application program interfacesconnected to the Internet, a plurality of data sources including RSSdata feeds and two or more of social data sources, entity web sites,forum web sites, blog web sites, or auction web sites, and receive,process, and extract data relating to entities with potential coverageneeds; analyze the extracted data and identify whether the extracteddata includes data indicative of an action by an entity associated withat least one of said plurality of social data sources that matches theat least first trigger rule; responsive to identification of the actionthat matches the at least first trigger rule, select one of theplurality of proactive underwriting templates, said one of the pluralityof proactive underwriting templates being associated with said at leastfirst trigger rule and defining a plurality of required data items forgenerating an underwriting determination; obtain, by the plurality ofapplication program interfaces via the network interface unit, from aplurality of data sources, the one or more of the plurality of requireddata items required in accordance with the template associated with theat least first trigger rule; perform a proactive underwriting analysisusing said collected data, said proactive underwriting analysis beingperformed without a request from said entity to perform saidunderwriting analysis; generate, by an underwriting module, a quotingmodule, and a rating schedule database coupled to the proactiveunderwriting platform processor, an underwriting determinationcomprising a price quotation for a risk mitigation product for theentity based on said proactive underwriting analysis; generate, by anotification module coupled to the proactive coverage generationplatform processor, an electronic communication including the pricequotation for the risk mitigation product; and transmit, by the networkinterface unit to a user device corresponding to the entity, theelectronic communication including the price quotation for the riskmitigation product.
 10. The computer system of claim 9, wherein theproactive underwriting platform processor is configured to apply naturallanguage processing to analyze the extracted data and identify whetherthe extracted data includes data indicative of the action by the entitythat matches at least the first trigger rule indicative of the coverageneed.
 11. The computer system of claim 9, wherein the data indicative ofan action by an entity that matches at least a first trigger ruleindicative of a coverage need is unrelated to any risk mitigationpolicy, application for risk mitigation coverage, or activity by anagent that sells risk mitigation coverage.
 12. The computer system ofclaim 9, wherein the proactive underwriting platform processor isfurther configured to access, before text mining the plurality of datasources, via the network interface unit implementing the plurality ofapplication program interfaces and without interaction with the entity,the plurality of data sources to obtain one or more of the plurality ofrequired data items required in accordance with the template associatedwith the at least first trigger rule.
 13. The computer system of claim9, wherein the proactive underwriting platform processor is configuredto apply a computerized predictive model trained to analyze relevantunderwriting parameter variables in a multivariable system to saidcollected data to perform the proactive underwriting analysis.
 14. Thecomputer system of claim 9, wherein the electronic communicationcomprises one of an electronic mail message, a social media networkdirect message, SMS messaging, or direct messaging.
 15. The computersystem of claim 9, wherein said at least first trigger rule is a ruleidentifying an insurable activity, and said selected proactive coveragegeneration underwriting template is a template for collecting data toperform an underwriting process for said insurable activity.
 16. Thecomputer system of claim 9, wherein the action is associated withgeographical data associated with a location of said entity, whereinsaid proactive underwriting template is further selected based on saidlocation of said entity.